Uterine fibroids, benign tumors of the human uterus, affect 77% of women by menopause in the U.S. and account for $5.9-34.4 billion in annual healthcare costs. Until recently, tumor tissue and cell culture studies investigating fibroid growth have been the primary sources for understanding fibroid pathophysiology. Genetic predisposition to fibroids has been documented in twin studies, where up to 69% of risk is heritable. We propose to identify common and rare variants associated with fibroid risk through GWAS, whole genome, and exome resequencing. We have convened a consortium of studies with GWAS data and imaging-confirmed fibroid cases and controls in the Electronic Medical Record and Genomics (eMERGE) Network. We have developed and validated a phenotyping algorithm in the BioVU DNA databank at Vanderbilt with stringent inclusion/exclusion criteria that requires pelvic imaging. Our phenotyping algorithm was implemented at seven eMERGE sites to generate harmonized phenotypes classified by common criteria across cases and controls with GWAS data in eMERGE. Among subjects with GWAS data, exome sequencing is available for 3,045 cases and 8,598 controls from Geisinger Health System MyCode Community Health Initiative (MyCode) and 500 cases and 500 controls from BioVU. We will conduct multi-stage transethnic GWAS and association studies in next-generation sequencing data for fibroid risk, leveraging existing genetic data, data generated by other funding (R01HD074711, R03HD078567), and new data from this application. Our Specific Aims are to: 1. Conduct a multi-stage transethnic EHR consortium meta-analysis of fibroid risk. We will conduct fixed effects meta-analysis of SNP summary statistics with 7,242 cases and 14,895 controls from eMERGE for Discovery. We will conduct in silico analysesof the strongest associated SNPs in over 7,000 cases and 14,000 controls for Replication. 2. Conduct a large-scale whole genome and exome resequencing study using BioVU and MyCode. We will use existing exome sequencing data for 3,645 cases and 9,098 controls from MyCode at 75X and BioVU at 50X to assess rare coding variants, and conduct whole genome sequencing at 15X in an additional 1,000 cases and 1,000 controls from BioVU for Discovery. We use 4,500 independent cases and 12,000 controls that will accrue in MyCode with exome sequencing, and additional existing 1,920 cases and 1,920 controls from BioVU with de novo genotyping of 50,000 SNPs for Replication of findings. 3. Evaluate the biological significance of associated variants in fibroid risk using bioinformatics approaches. We will use PrediXcan and PheWAS to further examine the SNPs/genes identified in Aims 1 and 2 for evidence of pleiotropy and mediation by regulation of gene expression. Little is known about the genetic causes of risk in fibroid disease. We propose an efficient and cost-effective approach to identify genetic risk factors for fibroids by taking advantage of available imaging, existing GWAS, sequencing, and DNA. This study will provide unparalleled genomic coverage for a large gold-standard collection of image-confirmed participants.